R Notebook

library(shiny)

Warning: package ‘shiny’ was built under R version 4.1.2

library(tidyverse)

Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
-- Attaching packages -------------------------------------------------------------------------------------------------------------------------------------------- tidyverse 1.3.1 --
v ggplot2 3.3.5     v purrr   0.3.4
v tibble  3.1.5     v dplyr   1.0.7
v tidyr   1.1.4     v stringr 1.4.0
v readr   2.0.2     v forcats 0.5.1
-- Conflicts ----------------------------------------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

library(shinythemes)

Warning: package ‘shinythemes’ was built under R version 4.1.2

library(stringi)
library(RColorBrewer)

seabirds_cleaned_data <- read_csv("clean_data/seabirds_cleaned_data.csv")

Rows: 49020 Columns: 52
-- Column specification -------------------------------------------------------------------------------------------------------------------------------------------------------------
Delimiter: ","
chr  (19): common_name, scientific_name, species_abbreviation, age, plphase, feeding, on_water, on_ice, on_ship, in_hand, fly_by, group_sighting, ship_wake, molting, nat_feeding...
dbl  (28): record_x, record_id, wanplum, total_sighting, num_feeding, num_on_water, num_on_ice, num_fly_by, num_group_sighting, num_ship_wake, record_y, lat, long, ship_activity...
lgl   (3): sex, air_temp, salinity
dttm  (2): date, time

i Use `spec()` to retrieve the full column specification for this data.
i Specify the column types or set `show_col_types = FALSE` to quiet this message.

birds_21 <- seabirds_cleaned_data %>%
  mutate(bird_type = case_when(
  str_detect(common_name, 
             regex("shearwater", 
                   ignore_case = TRUE)) ~ "Shearwater",
  str_detect(common_name, 
             regex("albatross", 
                   ignore_case = TRUE)) ~ "Albatross",
  str_detect(common_name, 
             regex("mollymawk", 
                   ignore_case = TRUE)) ~ "Mollymawk",
  str_detect(common_name, 
             regex("petrel", 
                   ignore_case = TRUE)) ~ "Petrel",
  str_detect(common_name, 
             regex("prion", 
                   ignore_case = TRUE)) ~ "Prion",
  str_detect(common_name, 
             regex("skua", 
                   ignore_case = TRUE)) ~ "Skua",
  str_detect(common_name, 
             regex("penguin", 
                   ignore_case = TRUE)) ~ "Penguin",
  str_detect(common_name, 
             regex("tropicbird", 
                   ignore_case = TRUE)) ~ "Tropicbird",
  str_detect(common_name, 
             regex("noddy", 
                   ignore_case = TRUE)) ~ "Noddy",
  str_detect(common_name, 
             regex("tern", 
                   ignore_case = TRUE)) ~ "Tern",
  str_detect(common_name, 
             regex("gull", 
                   ignore_case = TRUE)) ~ "Gull",
  str_detect(common_name, 
             regex("booby", 
                   ignore_case = TRUE)) ~ "Booby",
  str_detect(common_name, 
             regex("frigatebird", 
                   ignore_case = TRUE)) ~ "Frigatebird",
  str_detect(common_name, 
             regex("shag", 
                   ignore_case = TRUE)) ~ "Shag",
  str_detect(common_name, 
             regex("sheathbill", 
                   ignore_case = TRUE)) ~ "Sheathbill",
  str_detect(common_name, 
             regex("fulmar", 
                   ignore_case = TRUE)) ~ "Fulmar",
  str_detect(common_name, 
             regex("gannet", 
                   ignore_case = TRUE)) ~ "Gannet",
  str_detect(common_name, 
             regex("cormorant", 
                   ignore_case = TRUE)) ~ "Cormorant",
  str_detect(common_name, 
             regex("procellaria", 
                   ignore_case = TRUE)) ~ "Procellaria",
    TRUE ~ common_name))

birds_21

birds_21 %>% 
  arrange(date)

# https://r-charts.com/color-palette-generator/

# https://www.statology.org/color-by-factor-ggplot2/

birds_pal <- c("#50e2ea", "#4edae5", "#4bd2df", "#49cada", "#47c2d4", 
               "#45bbcf", "#42b3c9", "#40abc4", "#3ea3be", "#3b9bb9",
               "#3993b3", "#378bae", "#3483a8", "#327ba3", "#30739d", 
               "#2e6c98", "#2b6492", "#295c8d", "#275487", "#244c82", "#22447c")

names(birds_pal) <- levels(birds_21$bird_type)

custom_colors <- scale_colour_manual(values = birds_pal)
  

birds <- c("Tropicbird" = "#50e2ea", "Tern" = "#4edae5", "Skua" = "#4bd2df", 
           "Sheathbill" = "#49cada", "Shearwater" = "#47c2d4", 
           "Shag" = "#45bbcf", "Seabird" = "#42b3c9", "Procellaria" = "#40abc4",
           "Prion" = "#3ea3be", "Petrel" = "#3b9bb9", "Penguin" = "#3993b3", 
           "Noddy" = "#378bae", "Mollymawk" = "#3483a8", "Jaeger" = "#327ba3", 
           "Gull" = "#30739d", "Gannet" = "#2e6c98", "Fulmar" = "#2b6492", 
           "Frigatebird" = "#295c8d", "Cormorant" = "#275487", 
           "Booby" = "#244c82", "Albatross" = "#22447c")

Jaeger Seabird

```r
birds_9 %>% 
  filter(!is.na(bird_type)) %>% 
  count(bird_type)  

NA

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<div data-pagedtable="false">
  <script data-pagedtable-source type="application/json">
{"columns":[{"label":["bird_type"],"name":[1],"type":["chr"],"align":["left"]},{"label":["n"],"name":[2],"type":["int"],"align":["right"]}],"data":[{"1":"Albatross","2":"15681"},{"1":"Booby","2":"23"},{"1":"Cormorant","2":"9"},{"1":"Frigatebird","2":"6"},{"1":"Fulmar","2":"165"},{"1":"Gannet","2":"1679"},{"1":"Gull","2":"2366"},{"1":"Jaeger","2":"28"},{"1":"Mollymawk","2":"3661"},{"1":"Noddy","2":"39"},{"1":"Penguin","2":"70"},{"1":"Petrel","2":"14096"},{"1":"Prion","2":"2457"},{"1":"Procellaria","2":"2"},{"1":"Seabird","2":"2"},{"1":"Shag","2":"85"},{"1":"Shearwater","2":"6273"},{"1":"Sheathbill","2":"11"},{"1":"Skua","2":"704"},{"1":"Tern","2":"926"},{"1":"Tropicbird","2":"45"}],"options":{"columns":{"min":{},"max":[10],"total":[2]},"rows":{"min":[10],"max":[10],"total":[21]},"pages":{}}}
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```r
```r
sighting <-  birds_21 %>% 
            filter(!is.na(bird_type)) %>% 
            group_by(bird_type) %>% 
            summarise(count = sum(total_sighting, na.rm = TRUE)) %>%
            mutate(sighting_id = row_number())

sighting %>% 
    ggplot() +
    aes(y = bird_type, 
        x = count, fill = bird_type) +
    geom_col(colour = \black\) +
    theme(legend.position = \none\) +
    scale_x_continuous(breaks = c(1, 5, 10, 1000, 6000, 1400000),
                       limits = c(1,1400000), 
                       trans = \log10\) +
    labs(y = \\n Bird Names\,
         x = \Number of Birds Seen \n Log10 scale\) +
    scale_fill_manual(values = birds)

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<img 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RE0mGTw5XvatwGy6EFb+aYh5W/J2xytbrIVslbZttZJg+8PXt1l14O28k7D70AZAg2QVnNpSGQmlvU6vZqH7+NdbzWWhmRmYrkpo7aL7+NdbzWWhnhOQayuCdvtzkg8iK/DjdvpdzLyfbzrrXokKaLyNMSZCbG6JuyqB7G6BrHBVs2ODWEgi8HJ9IRaXQMabNVYGuLMhFhdo2kQq2tAg60aS0OcmRCrazQNYuPsKL0mnO/jXW81l4Z8GXyBhkxtGw1Qpux60Fa+aUDWypl7IlvrpMH3Z6+qsroDNJSm4bcbLdAAGrRAQ5p41qo9/+I8aNhyGg7mT0FD2rHNbrBRrWlQ35lP3NFokFUTznd/VlNtskquZXWm6IwUDck7GmXVhPPdn9WE2JBKw0JsWLijUUbdJ9/9WU2gIZeGYYIGlcQCDUUabFRrGvR6Gn1HI9BQsMFGNaUhXwZfoKGqNoqGhiurO0DDOmmgORjIWmXbWisNX7IWaCA3BhpAQ7Yt0EBjYLtomKepFtIS8bqqMNkw7oMGSpeujFWi4cCwUX0/eoEQ0EDr0pWx6jSE+53TQNy1SGSoxu1ufPcikbRgrZ/y+ajUtVYVaMjdQYhMxc4UrMe76q5FIkPFYoO6exGfkRQLrtSdCQzkITY4c09kqzANKjERBuKuRSJDxWhQdy+aHIgZatEGGihdujJGRIP4/IsMlYwNbHMcGwLEBnqXrowR0SBzVTxDNTvqyrsXxeOGuC4caKB06coY0XzD6l2LTDL4Ag3O3BPZsqBh9a5FRWlAnsKVeyJbFjQUk8GXJQXaFGhwbGutNHyxtECDM2OgATRk2wINNAa2jAY+GZl/JRFlVggEDa7cE9kqSsP0g5Gs75P7woxKP6DBlXsiW0VpiNOUsyORpOqcxsur1O//Pq8VmFUh0IKGfPwgauUf9Dj7MA7Yfz4RHS+vUr/rWoGIDcQuXRmrHBv+b3QyEPmpKLGgRvyuawWCBmKXroxVGjfwWsJHcSzQNKjfda1A0EDs0pUxgmsKOU7QNMx/n9cKzKoQCBpcuSeyVZiGsjL4Ag3O3BPZWisNyFO4ck9kq+40VJGDowka6Gj4rbUKNJS2BRoqH0LLBlL3RLZAQ+VDaNlA6p7IljUN7Jpy4StQy6XhDL5AgzP3RLbsaWC9fzJY/B00OHDpyhg5DZ/IrFWcump3RsNAfJ0+LWu1ZhrK/1EQl+WZojMSM9PzVFYw/WCkboVnIA+xwZl7Ilv2NAQ8oymyVip1NTmIhn21fNfgCzQ4c09kqxoN/aXYINEADZQuXRlzcE2xNG5gA8vvnIMGUpeujK1hvmF2pL47afAFGpy5J7JFS0PY7qqnBl/IUzhzT2SLloaEDL7WTUMTVb4HbeWdht+EcgQaIC3QAGk1i4ZhNxJlXOYbXqet3Tf4Ag25ahQN0+99b7RAg/qGNGigUaNoGAeyBty+uovVuN0N9zuf8emn6SO2UTykZq18H+v6qxaZtoI7MfvRYPLwPK4IqGoE8hVWgeRk9l/iQbzWQB5oyFWTYgNfU7E7EGeKyYGqESjX1kwOWFjYHYgH0GCtJtHAi3/FZUJF2chhTIOIDdFivWmDL9CQqwbRwFfts4fTuCJgXCNQrsK8dz6WK7TFuAE0WKpBNJSSwRdoyNU20QDlqnwP2mqtNBhkuuos9epyBlyo/B743+cUpe8YaCgo0LAG/wVf7f/IggZoywQaIC3QAGmBBkhrLTSMC5WilZodqXxH8u1sa9f8etdSzvVDttTKguLvcC5x+CcPl/fKsH/roGFYrDCx1PQHpreHQZH7pDjQ3Ll+yH4DO/Tjfql3uJY4/LMjUbNtuLxri/u3Bhqm/1YmNkweLN0RRbz91WBlKfhapJ3rh+x3fPIPbP9LvcOx5OEfv/t4ea8M+1e7MwXPmT9ZefuJt4OqnOuH7NcP+wsv9rfjWuxPmDz878fnS3tl2L/a0RCtFoTwGBu088KxYZTx2fMjfvLid79uYGzgX7jrr27ydvodlx83hPUaN8jDL7+P4n/cUPqaYnkQjmuKalI01OOaAmqKQAOkBRogLdAAaYEGSAs0QFrbScPl7cPo+tnh0sY7L1Nefv201WPtt1ot9nP+suunPf2SC9HWdG0pDbd2ytAgW/jj1Vv6TUkaLr/6XCDWcG0pDXf+8ZDRwPqXP/7Bt1p/8rR1l//cEZ/ynYg9v/GcvfCs1bobXe21+C+Chr99eXnnBW88a33hWz0WNFh7pEkSb45N/OEef94obSsNL/5Y03Bb/L/z4hbr3R7v1+Ne/DlnH3keDeLYwM8UO/y9rJG1XN7qXexEV/c5NdHxHJhjZYK9V7Y1R9tKw8uf//Ochjsvxf8XLNqf3eUDABYn5Ged9Tbr18UzBXshe3J2l7cwQHaUzau9G8/lm+cmVk5GddfW0nD5R4ID0b0LNOzEL+A/VmgQn3xNgwgY89GDevPcBGhohEQsf+OQh/vbmgZ5prh9GJ8vouUzBY8N959LGnjLXu9shzHBBw4X7Dyh36xMgIZGiHcsHxoct77wN5qG5CgyHhWeievGxLiBBwpOA2t542s9ZqB182X8Qj6eTI4iQQPUaIEGSAs0QFqgAdICDUsyzU+LWaSL1ERE+pR20wQalmToWj6vJOadvmqeWgQNG6u4a8/iC8Yb/9S7/uHHLDbMLxjj3AS73mSMnMXXkvLKsvECDUuK5xbEtBP/v9eTZ4p4WlLMNLLf+aTU2Q6fzFTzUcfNT2iDhmVJGmTfX8advECDzE2oycpbrTcOL+/8qtWKk5nNFmhYUhoNialFnpuIX3bjuUh0/GrH4x4TCjQsKe1MMR9FytwEP1Nc3GQUXPDY8EJkJjzvOYFAw5LibzHIBAUbRf5db/kKU+YmxCjyaq/1G3s9jCK3RY1LPFUSaMgQ++hvwtiwuEADpAUaIC3QAGmBBkgLNEBa/w+n5nlaykGnbQAAAABJRU5ErkJggg==" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG4jIGxvZzEwKCkgYXMgMSBvciBtb3JlIGJpcmRzIGFyZSBsZXNzIHRoYW4gMTAgYW5kIGRvbid0IHNob3cgb24gbm9ybWFsIGdyYXBoXG5gYGBcbmBgYCJ9 -->

```r
```r

# log10() as 1 or more birds are less than 10 and don't show on normal graph

<!-- rnb-source-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->

1,394,468


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin 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 -->

```r
```r
feeding <-  birds_21 %>% 
              group_by(bird_type) %>% 
              filter(str_detect(feeding, \YES\)) %>% 
              summarise(count = n()) %>% 
              mutate(feeding_id = row_number())

feeding %>% 
    ggplot() +
    aes(y = bird_type, 
        x = count, fill = bird_type) +
    geom_col(colour = \black\) +
    theme(legend.position = \none\) +
    scale_x_continuous(breaks = c(1, 5, 10, 100, 300, 800),
                       limits = c(1,800), 
                       trans = \log10\) +
    labs(y = \\n Bird Names\,
         x = \Number of Birds Seen Feeding \n Log10 scale\) +
    scale_fill_manual(values = birds)

<!-- rnb-source-end -->

<!-- rnb-plot-begin -->

<img src="data:image/png;base64,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/>

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```r
```r
# log10() as 1 or more birds are less than 10 and don't show on normal graph

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```r
on_ship <-  birds_21 %>% 
              group_by(bird_type) %>% 
              filter(str_detect(on_ship, "YES")) %>% 
              summarise(count = n()) %>% 
              mutate(on_ship_id = row_number())

on_ship %>% 
    ggplot() +
    aes(y = bird_type, 
        x = count, fill = bird_type) +
    geom_col(colour = "black") +
    theme(legend.position = "none") +
    scale_x_continuous(breaks = c(1, 2, 3, 5, 7, 10, 60),
                       limits = c(1,60), 
                       trans = "log10") +
    labs(y = "\n Bird Names",
         x = "Number of Birds Seen On Ship") +
    scale_fill_manual(values = birds)

```r
in_hand <-  birds_21 %>% 
              group_by(bird_type) %>% 
              filter(str_detect(in_hand, \YES\)) %>% 
              summarise(count = n()) %>% 
              mutate(in_hand_id = row_number()) 

in_hand %>% 
    ggplot() +
    aes(y = bird_type, 
        x = count, fill = bird_type) +
    geom_col(colour = \black\) +
    theme(legend.position = \none\) +
    labs(y = \\n Bird Names\,
         x = \Number of Birds Seen In Hand\) +
    scale_fill_manual(values = birds)

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<!-- rnb-source-begin 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 -->

```r
# https://stackoverflow.com/questions/14255533/pretty-ticks-for-log-normal-scale-using-ggplot2-dynamic-not-manual
# https://stackoverflow.com/questions/43974892/dynamic-limits-and-breaks-in-scale-y-continuous


fly_by <-  birds_21 %>% 
              group_by(bird_type) %>% 
              filter(str_detect(fly_by, "YES")) %>% 
              summarise(count = n()) %>% 
              mutate(fly_by_id = row_number())


# base_breaks <- function(n = 10){
#     function(x) {
#         axisTicks(log10(range(fly_by$count, na.rm = TRUE)), 
#                   log = if_else(max(fly_by$count) > 1000, TRUE, FALSE), n = n)
#     }
# }

# asd <- if_else(max(fly_by$count) < 1000, c(limits=c(0,max(fly_by$count)), 
#                                            breaks  = seq(0,max(fly_by$count),
#                                             by = round(max(fly_by$count)/5))), 
#                c(limits=c(0,max(fly_by$count)), 
#                  breaks  = seq(0,max(fly_by$count), 
#                   by = round(max(fly_by$count)/5)),
#                  trans = "log10")
#                )

fly_by %>% 
    ggplot() +
    aes(y = bird_type, 
        x = count, fill = bird_type) +
    geom_col(colour = "black") +
    theme(legend.position = "none") +
    scale_x_continuous(limits=c(0,max(fly_by$count)), 
                       breaks  = c(seq(0,max(fly_by$count),
                        by = (max(fly_by$count)/5))), trans = "log10"
                       #validate(max(fly_by$count) < 1000, trans = "log10")
                       ) +
    labs(y = "\n Bird Names",
         x = "Number of Birds Seen Flying BY\n Log10 scale") +
    scale_fill_manual(values = birds)

Error in seq.default(a, b, length.out = n + 1) : 
  'from' must be a finite number

breaks = c(1, 5, 10, 1000, 6000), limits = c(1,6000), trans = “log10”

variants <- birds_21 %>% 
              filter(bird_type == "Albatross") %>% 
              group_by(common_name) %>% 
              summarise(count = n())
  
variants %>% 
    ggplot() +
    aes(y = common_name, 
        x = count, fill = common_name) +
    geom_col(colour = "black") +
    theme(legend.position = "none") +
    scale_x_continuous(
                       trans = "log10") +
    labs(y = "\n Bird Names",
         x = "Number of Birds Seen Flying BY\n Log10 scale") +
    scale_fill_manual(values = birds_pal)            

```r
variants <- birds_21 %>% 
              filter(bird_type == \Booby\) %>% 
              group_by(common_name) %>% 
              summarise(count = n())
  
variants %>% 
    ggplot() +
    aes(y = common_name, 
        x = count, fill = common_name) +
    geom_col(colour = \black\) +
    theme(legend.position = \none\) +
    scale_x_continuous( 
                       trans = \log10\) +
    labs(y = \\n Bird Names\,
         x = \Number of Birds Seen Flying BY\n Log10 scale\) +
    scale_fill_manual(values = birds_pal)  

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```r
```r
variants <- birds_21 %>% 
              filter(bird_type == \Cormorant\) %>% 
              group_by(common_name) %>% 
              summarise(count = n())
  
variants %>% 
    ggplot() +
    aes(y = common_name, 
        x = count, fill = common_name) +
    geom_col(colour = \black\) +
    theme(legend.position = \none\) +
    scale_x_continuous( 
                       trans = \log10\) +
    labs(y = \\n Bird Names\,
         x = \Number of Birds Seen Flying BY\n Log10 scale\) +
    scale_fill_manual(values = birds_pal) 

<!-- rnb-source-end -->

<!-- rnb-plot-begin -->

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<!-- rnb-plot-end -->

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```r
```r
variants <- birds_21 %>% 
              filter(bird_type == \Tropicbird\) %>% 
              group_by(common_name) %>% 
              summarise(count = n())
  
variants %>% 
    ggplot() +
    aes(y = common_name, 
        x = count, fill = common_name) +
    geom_col(colour = \black\) +
    theme(legend.position = \none\) +
    scale_x_continuous() +
    labs(y = \\n Bird Names\,
         x = \Number of Birds Seen Flying BY\n Log10 scale\) +
    scale_fill_manual(values = birds_pal) 

<!-- rnb-source-end -->

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<img src="data:image/png;base64,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/>

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```r
```r
```r
library(shiny)
library(tidyverse)
library(shinythemes)


seabirds_cleaned_data <- read_csv(\data/seabirds_cleaned_data.csv\)

Warning: package ‘leaflet’ was built under R version 4.1.2

# birds_9 <- seabirds_cleaned_data %>% 
#   group_by(common_name) %>% 
#   mutate(common_name = if_else(str_detect(common_name, 
#                                           "(?i)shearwater"),"Shearwater", 
#                                common_name),
#          common_name = if_else(str_detect(common_name, 
#                                           "(?i)albatross"), "Albatross",
#                                common_name),
#          common_name = if_else(str_detect(common_name, 
#                                           "(?i)mollymawk"), "Mollymawk",
#                                common_name),
#          common_name = if_else(str_detect(common_name, 
#                                           "(?i)petrel"), "Petrel",
#                                common_name),
#          common_name = if_else(str_detect(common_name, 
#                                           "(?i)prion"), "Prion",
#                                common_name),
#          common_name = if_else(str_detect(common_name, 
#                                           "(?i)skua"), "Skua",
#                                common_name),
#          common_name = if_else(str_detect(common_name, 
#                                           "(?i)penguin"), "Penguin",
#                                common_name),
#          common_name = if_else(str_detect(common_name, 
#                                           "(?i)Red-tailed tropicbird"), 
#                                "Red-tailed tropicbird",
#                                common_name),
#          common_name = if_else(str_detect(common_name, 
#                                           "(?i)Brown noddy"), "Brown noddy",
#                                common_name)
#   ) %>% 
#   filter(common_name %in% c("Shearwater", "Albatross", 
#                             "Mollymawk", "Petrel", 
#                             "Prion", "Skua", 
#                             "Penguin", "Brown noddy", 
#                             "Red-tailed tropicbird"))

# pal <- c("Shearwater" = "grey", "Albatross" = "blue", 
#          "Mollymawk" = "yellow", "Petrel" = "green", 
#          "Prion" = "pink", "Skua" = "purple", 
#          "Penguin" = "orange", "Brown noddy" = "brown", 
#          "Red-tailed tropicbird" = "red")

# names(birds_9)
# head(birds_9)

# birds_9 %>% 
#   group_by(common_name) %>% 
#   mutate(feeding = if_else(feeding %in% "YES", 1, 0),
#          on_ship = if_else(on_ship %in% "YES", 1, 0),
#          in_hand = if_else(in_hand %in% "YES", 1, 0),
#          fly_by = if_else(fly_by %in% "YES", 1, 0)) %>% 
#   summarise(sighting_count = sum(total_sighting, na.rm = TRUE),
#             feeding_count = sum(feeding, na.rm = TRUE),
#             on_ship_count = sum(on_ship, na.rm = TRUE),
#             in_hand_count = sum(in_hand, na.rm = TRUE),
#             fly_by_count = sum(fly_by, na.rm = TRUE)) 

```r
tail(position)

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<div data-pagedtable="false">
  <script data-pagedtable-source type="application/json">
{"columns":[{"label":["date"],"name":[1],"type":["S3: POSIXct"],"align":["right"]},{"label":["lat"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["long"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"1989-09-17","2":"-38.00000","3":"141.0000"},{"1":"1989-09-24","2":"-34.00000","3":"150.7500"},{"1":"1989-10-29","2":"-34.00000","3":"150.7500"},{"1":"1990-12-19","2":"-51.54000","3":"155.8267"},{"1":"1990-12-20","2":"-47.74444","3":"151.8481"},{"1":"1990-12-21","2":"-44.11389","3":"148.3750"}],"options":{"columns":{"min":{},"max":[10],"total":[3]},"rows":{"min":[10],"max":[10],"total":[6]},"pages":{}}}
  </script>
</div>

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```r

  leaflet(data = birds_21) %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(label = birds_21$common_name, clusterOptions = markerClusterOptions()) 

Assuming "long" and "lat" are longitude and latitude, respectively
Warning in validateCoords(lng, lat, funcName) :
  Data contains 30 rows with either missing or invalid lat/lon values and will be ignored

  
 # Print the map

-45.91667 165.4000

seabirds_cleaned_data

ship_data <- read_excel(here("raw_data/seabirds.xls"), 
                             sheet = "Ship data by record ID") %>% 
              clean_names()

position <- ship_data %>% 
  select(date, lat, long) %>%
  filter(!is.na(lat),
         !is.na(long)) %>% 
  group_by(date) %>% 
  summarise_if(is.numeric, mean)

position

ship_data %>% 
  select(date, lat, long) %>%
  filter(is.na(date))

```r
library(dplyr)
library(shiny)
library(leaflet)
library(readxl)
library(RColorBrewer)
library(maps)
library(leaflet.extras)
library(htmlwidgets)



data_dots = read_csv(\test4.csv\)

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Rows: 8 Columns: 14 – Column specification ————————————————————————————————————————————————————- Delimiter:

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```r
```r
ui <- bootstrapPage(
  tags$style(type = \text/css\, \html

Warning: Couldn't coerce the `end` argument to a date string with format yyyy-mm-dd

```r
server <- function(input, output) {

  #n <- 60
  qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual', ]
  col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))


  myMap = leaflet(\map\) %>% 
    addTiles(group = \Base\) %>%
    addProviderTiles(providers$CartoDB.Positron, group = \Grey\) %>%
    addResetMapButton()


  rv <- reactiveValues(
    filteredData =data_dots,
    ids = unique(data_dots$Route)
  )

  observeEvent(input$dateRange, 
               {rv$filteredData = data_dots[as.Date(data_dots$ship_date) >= input$dateRange[1] & as.Date(data_dots$ship_date) <= input$dateRange[2],]

               rv$ids = unique(rv$filteredData$Route)
               }

  )



  # Initiate the map
  output$map <- renderLeaflet({

    for (i in rv$ids) {
      #print(i)
      myMap = myMap %>%
        addPolylines(
          data = subset(rv$filteredData, Route == i),
          weight = 3,
          color = sample(col_vector, 1),
          opacity = 0.8,
          smoothFactor = 1,
          lng = ~Dlong, 
          lat = ~Dlat,
          highlight = highlightOptions(
            weight = 5,
            color = \blue\,
            bringToFront = TRUE
          ),
          label = ~ as.character(ShipmentID),
          popup = ~ as.character(ShipmentID),
          group = \test\
        )

    }
    myMap


  })


}
shinyApp(ui = ui, server = server)

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<!-- rnb-output-begin eyJkYXRhIjoiXG5MaXN0ZW5pbmcgb24gaHR0cDovLzEyNy4wLjAuMTo0Mjc3XG5XYXJuaW5nOiBFcnJvciBpbiBjaGFyVG9EYXRlOiBjaGFyYWN0ZXIgc3RyaW5nIGlzIG5vdCBpbiBhIHN0YW5kYXJkIHVuYW1iaWd1b3VzIGZvcm1hdFxuICBbTm8gc3RhY2sgdHJhY2UgYXZhaWxhYmxlXVxuTkFcbiJ9 -->

Listening on http://127.0.0.1:4277 Warning: Error in charToDate: character string is not in a standard unambiguous format [No stack trace available] NA

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```r
```r
data_dots %>% 
  mutate(ship_date = as.Date(ship_date, \%y/%m/%d\),
         delivery_date = as.Date(delivery_date, \%y/%m/%d\))

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# https://kateto.net/network-visualization
# https://stackoverflow.com/questions/38432788/how-do-i-visualise-multiple-routes-using-leaflet-in-r



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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```r
# https://rstudio.github.io/leaflet/markers.html
# first 20 quakes
df.20 <- quakes[1:20,]

getColor <- function(quakes) {
  sapply(position$date, function(date) {
  if(date <= 1979-12-31) {
    "green"
  } else if(date <= 1989-12-31) {
    "orange"
  } else {
    "red"
  } })
}

icons <- awesomeIcons(
  icon = 'ios-close',
  iconColor = 'black',
  library = 'ion',
  markerColor = getColor(position)
)

leaflet(position) %>% addTiles() %>%
  addAwesomeMarkers(~long, ~lat, icon=icons, label=~as.character(date))

getColor <- function(quakes) {
  sapply(position$date, function(date) {
  if(date %in% "^196") {
    "green"
  } else if(date  %in% "^197") {
    "orange"
  } else if(date  %in% "^198") {
    "blue"
  } else {
    "red"
  } })
}

icons <- awesomeIcons(
  icon = 'ios-close',
  iconColor = 'black',
  library = 'ion',
  markerColor = getColor(position)
)

leaflet(position) %>% addTiles() %>%
  addAwesomeMarkers(~long, ~lat, icon=icons, label=~as.character(date))

getColor <- function(quakes) {
  sapply(position$date, function(date) {
  case_when(str_detect(date, 
             regex("^196", 
                   ignore_case = TRUE)) ~ "green",
            str_detect(date, 
             regex("^197", 
                   ignore_case = TRUE)) ~ "orange",
             str_detect(date, 
             regex("^198", 
                   ignore_case = TRUE)) ~ "blue",
             str_detect(date, 
             regex("^199", 
                   ignore_case = TRUE)) ~ "red"
    
  ) })
}

icons <- awesomeIcons(
  icon = 'ios-close',
  iconColor = 'black',
  library = 'ion',
  markerColor = getColor(position)
)

leaflet(position) %>% addTiles() %>%
  addAwesomeMarkers(~long, ~lat, icon=icons, label=~as.character(date))

# https://stackoverflow.com/questions/56362519/how-to-filter-date-range-for-routes-in-r-leaflet-shiny-app

library(dplyr)
library(shiny)
library(leaflet)
library(readxl)
library(RColorBrewer)
library(maps)
library(leaflet.extras)
library(htmlwidgets)



data_dots = read_csv("test4.csv")


ui <- bootstrapPage(
  tags$style(type = "text/css", "html, body {width:100%;height:100%}"),
  leafletOutput("map", width = "100%", height = "100%"),
  absolutePanel(top = 10, right = 10,

                dateRangeInput("dateRange", "Date Range Input", start =  min(data_dots$ship_date), end = max(data_dots$ship_date))


  )
)


server <- function(input, output) {

  #n <- 60
  qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual', ]
  col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))


  myMap = leaflet("map") %>% 
    addTiles(group = "Base") %>%
    addProviderTiles(providers$CartoDB.Positron, group = "Grey") %>%
    addResetMapButton()


  rv <- reactiveValues(
    filteredData =data_dots,
    ids = unique(data_dots$Route)
  )

  observeEvent(input$dateRange, 
               {rv$filteredData = data_dots[as.Date(data_dots$ship_date) >= input$dateRange[1] & as.Date(data_dots$ship_date) <= input$dateRange[2],]

               rv$ids = unique(rv$filteredData$Route)
               }

  )



  # Initiate the map
  output$map <- renderLeaflet({

    for (i in rv$ids) {
      #print(i)
      myMap = myMap %>%
        addPolylines(
          data = subset(rv$filteredData, Route == i),
          weight = 3,
          color = sample(col_vector, 1),
          opacity = 0.8,
          smoothFactor = 1,
          lng = ~Dlong, 
          lat = ~Dlat,
          highlight = highlightOptions(
            weight = 5,
            color = "blue",
            bringToFront = TRUE
          ),
          label = ~ as.character(ShipmentID),
          popup = ~ as.character(ShipmentID),
          group = "test"
        )

    }
    myMap


  })


}
shinyApp(ui = ui, server = server)

data_dots %>% 
  mutate(ship_date = as.Date(ship_date, "%y/%m/%d"),
         delivery_date = as.Date(delivery_date, "%y/%m/%d"))